Proposes sBDCA with preconditioning for the LTS estimator, claiming up to 3.25 times faster runtime and up to 90% lower objective values than Fast-LTS on synthetic and real data.
The Boosted Difference of Convex Func- tions Algorithm for nonsmooth functions
2 Pith papers cite this work. Polarity classification is still indexing.
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A nonmonotone subgradient algorithm is developed for upper-C^2 optimization on submanifolds with stationarity and KL-based convergence guarantees.
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Faster than Fast-LTS: Robust Regression and Outlier Detection with DC Programming
Proposes sBDCA with preconditioning for the LTS estimator, claiming up to 3.25 times faster runtime and up to 90% lower objective values than Fast-LTS on synthetic and real data.
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A Nonmonotone Descent Method for Optimization Problems Defined by Upper-$\mathcal{C}^2 $ Functions over Submanifolds
A nonmonotone subgradient algorithm is developed for upper-C^2 optimization on submanifolds with stationarity and KL-based convergence guarantees.